Interobserver diagnostic reproducibility in advanced-stage endometrial carcinoma

Deep Learning for Grading Endometrial Cancer

Endometrial cancer is the fourth most common cancer in women in the United States; the lifetime risk for developing this disease is approximately 2.8%. Precise histologic evaluation and molecular classification of endometrial cancer are important for effective patient management and determining the best treatment modalities. This study introduces EndoNet, which uses convolutional neural networks for extracting histologic features and a vision transformer for aggregating these features and classifying slides based on their visual characteristics into high- and low-grade cases. The model was trained on 929 digitized hematoxylin and eosin-stained whole-slide images of endometrial cancer from hysterectomy cases at Dartmouth-Health. It classifies these slides into low-grade (endometrioid grades 1 and 2) and high-grade (endometrioid carcinoma International Federation of Gynecology and Obstetrics grade 3, uterine serous carcinoma, or carcinosarcoma) categories. EndoNet was evaluated on an internal test set of 110 patients and an external test set of 100 patients from The Cancer Genome Atlas public database. The model achieved a weighted average F1 score of 0.91 (95% CI, 0.86 to 0.95) and an area under the curve of 0.95 (95% CI, 0.89 to 0.99) on the internal test, and 0.86 (95% CI, 0.80 to 0.94) for F1 score and 0.86 (95% CI, 0.75 to 0.93) for area under the curve on the external test. Pending further validation, EndoNet has the potential to support pathologists without the need of manual annotations in classifying the grades of gynecologic pathology tumors.

Accuracy of endometrial sampling in the diagnosis of endometrial cancer: a multicenter retrospective analysis of the JAGO-NOGGO

BACKGROUND

Accurate preoperative molecular and histological risk stratification is essential for effective treatment planning in endometrial cancer. However, inconsistencies between pre- and postoperative tumor histology have been reported in previous studies. To address this issue and identify risk factors related to inaccurate histologic diagnosis after preoperative endometrial evaluation, we conducted this retrospective analysis.

METHODS

We conducted a retrospective analysis involving 375 patients treated for primary endometrial cancer in five different gynaecological departments in Germany. Histological assessments of curettage and hysterectomy specimens were collected and evaluated.

RESULTS

Preoperative histologic subtype was confirmed in 89.5% of cases and preoperative tumor grading in 75.2% of cases. Higher rates of histologic subtype variations (36.84%) were observed for non-endometrioid carcinomas. Non-endometrioid (OR 4.41) histology and high-grade (OR 8.37) carcinomas were identified as predictors of diverging histologic subtypes, while intermediate (OR 5.04) and high grading (OR 3.94) predicted diverging tumor grading.

CONCLUSION

When planning therapy for endometrial cancer, the limited accuracy of endometrial sampling, especially in case of non-endometrioid histology or high tumor grading, should be carefully considered.

Application of NGS molecular classification in the diagnosis of endometrial carcinoma: A supplement to traditional pathological diagnosis

OBJECTIVE

This study aims to demonstrate the advantages of NGS molecular classification in EC diagnosis and to assess whether molecular classification could be performed on curettage specimens and its concordance with subsequent hysterectomy specimens.

METHODS

80 patients with hysterectomy specimens and 35/80 patients with paired curettage specimens were stratified as POLE mut, MSI-H, TP53 wt, or TP53 abn group by NGS panel. Histotype, tumor grade, IHC results, and other pathological details were taken from original pathological reports.

RESULTS

The correlation analysis of 80 patients with hysterectomy specimens between NGS molecular classification and clinicopathological characters displayed that the POLE mut group was associated with EEC (87.5%) and TP53 abn subtype was correlated to a later stage (Stage II-IV, 47.6%), G3 (76.2%), serous histology (61.9%) and myometrial invasion ≥50% (47.6%). A favorable concordance (31/32, 96.9%) was shown in MSI analysis and MMR IHC results, and the agreement rate of p53 IHC and TP53 mutation was 81.5% (53/65). Compared with the p53 IHC abnormal group, the TP53 mutation group had a higher correlation with high-risk factors. A high level of concordance (31/35, 88.0%) of NGS molecular classification was achieved between curettage specimens and hysterectomy specimens while grade and histotype (including unclassified group) from curettage specimens and hysterectomy specimens showed only moderate levels of agreement, 54.3% (19/35) and 68.6% (24/35), respectively.

CONCLUSION

NGS molecular classification achieved on curettage samples showed high concordance with the final hysterectomy specimens, demonstrating superior to the conventional pathological assessment of grade and histotype and potential utilization in clinical practice.

Risk Stratification of Endometrial Cancer Patients: FIGO Stage, Biomarkers and Molecular Classification

Endometrial cancer (EC) is the most common gynaecologic malignancy in developed countries. The main challenge in EC management is to correctly estimate the risk of metastases at diagnosis and the risk to develop recurrences in the future. Risk stratification determines the need for surgical staging and adjuvant treatment. Detection of occult, microscopic metastases upstages patients, provides important prognostic information and guides adjuvant treatment. The molecular classification subdivides EC into four prognostic subgroups: POLE ultramutated; mismatch repair deficient (MMRd); nonspecific molecular profile (NSMP); and TP53 mutated (p53abn). How surgical staging should be adjusted based on preoperative molecular profiling is currently unknown. Moreover, little is known whether and how other known prognostic biomarkers affect prognosis prediction independent of or in addition to these molecular subgroups. This review summarizes the factors incorporated in surgical staging (i.e., peritoneal washing, lymph node dissection, omentectomy and peritoneal biopsies), and its impact on prognosis and adjuvant treatment decisions in an era of molecular classification of EC. Moreover, the relation between FIGO stage and molecular classification is evaluated including the current gaps in knowledge and future perspectives.